Priority Queuing Based Spectrum Sensing Methodology in Cognitive Radio Network

(1)

Thesis Supervisor: Dr. Jorgen Nordberg Thesis Examiner: Dr. Jorgen Nordberg School of Engineering

Blekinge Institute of Technology

Priority Queuing Based Spectrum Sensing Methodology in Cognitive Radio Network

Thesis By

Name: Mujeeb Abdullah PNo: 8401079531

Name: Sajid Mahmood PNo: 8411296133

This thesis is submitted to the school of Engineering as part of

partial fulfillment for the Degree of Master Science in Electrical

Engineering.

(2)

(3)

Abstract

Radio spectrum is becoming scarce resource due to increase in the usage of wireless communication devices. However studies have revealed that most of the allotted spectrum is not used effectively. Given the demand for more bandwidth and the amount of underutilized spectrum, DSA (Dynamic Spectrum Access) networks employing cognitive radios are a solution that can revolutionize the telecommunications industry, significantly changing the way we use spectrum resources, and design wireless systems and services. Cognitive radio has improve the spectral efficiency of licensed radio frequency bands by accessing unused part of the band opportunistically without interfering with a license primary user PU. In this thesis we investigate the effects on the quality of service (QoS) performance of spectrum management techniques for the connection-based channel usage behavior for Secondary user (SU). This study also consider other factors such as spectrum sensing time, spectrum handoff and generally distributed service time and channel contention for different SUs. The preemptive resume priority M/G/1 queuing theory is used to characterize the above mentioned effects. The proposed structure of the model can integrate various system parameters such spectrum sensing, spectrum decision, spectrum sharing and spectrum handoff.

(4)

DEDICATION

To Parents.

(5)

Acknowledgements

We would like to thank Prof Dr.Jorgen Nordberg, my thesis supervisor, for all his support, encouragement and advice since we start our thesis, for their guidance and support throughout the course of this research. Thanks also go to my friends and colleagues and the department faculty and staff for making my time at a great experience. Finally, thanks to our families for their encouragement.

(6)

List of Figure

Figure 1.1Measurement form 0-6 GHz spectrum utilization at BWRC [29]. ... 10

Figure 2.1: Schematic representation of division of the radio spectrum and radio ranges. ... 12

Figure 2.2 : Radio spectrum measurements [3]. ... 13

Figure 2.3: Cognitive Radio Cycle ... 14

Figure 2.4: Cognitive Radio Network Model ... 15

Figure 2.5 : XG1™ Cognitive Radio ... 17

Figure 2.6 : Network Architecture of xMax cognitive radio network. ... 18

Figure 3.1 Basic Queuing Model. ... 20

Figure 3.2 : FIFO Queuing Model. ... 21

Figure 3.3 : Priority Queuing Model. ... 22

Figure 3.4 : Fair Queuing Model. ... 23

Figure 3.5 : M/M/1 Queuing Model. ... 24

Figure 3.6 : Exponential traffic arrival distribution. ... 24

Figure 3.7 : M/G/1 Priority Queuing Model ... 25

Figure 3.8 : Graphical representation of Call loss ratio experienced by users with different Queuing Distribution systems... 29

Figure 3.9: Simulation Strategies for Model selection. ... 30

Figure 4.1 Cognitive radio Network Architecture. ... 33

Figure 4.2 : Relationship between spectrum sensing, spectrum decision, and spectrum sharing and spectrum mobility. ... 37

Figure 5.1 : Cognitive radio network using TDMA slot for communication. ... 40

Figure 5.2 Queuing model for multiple PUs and single SU. ... 41

Figure 5.3:Single PU and single SU channel access. ... 43

Figure 5.4: Queuing model for single PU and single SU. ... 44

Figure 5.5 : Multiple PU and single SU Vs PU arrival rate. ... 48

Figure 5.6 : Single PU and Single SU Vs PU arrival rate. ... 49

Figure 5.7 : The average waiting time for packets arriving to each priority queue with equal rate for primary and secondary users. ... 50

Figure 5.8 : The average waiting time for packets arriving to each priority queue with varying rate of secondary user and fixed rate for primary users. ... 51

Figure 5.9 : The analytical model for spectrum selection scheme using PRP-M/G/1 queuing. .... 53

Figure 5.10 : The overall transmission time of the secondary user in the cognitive radio network. ... 54

Figure 5.11: Comparison of the Overall(Average) transmission time (T) for three different channel selection schemes. ... 57

Figure 5.12 : Overall (Average) transmission time T is plotted against SU traffic arrival rate for three different schemes. Traditional based sensing scheme is used as a reference for comparison and optimal sensing performs better in terms of Overall (average) transmission time. ... 58

Figure 5.13 : : Probability based approach vs Instantaneous sensing based approach. ... 59

Figure 5.14 : Stacked bar representation. Probability based spectrum selection scheme based optimal distribution vector is varied with SU traffic arrival rate. Probability of each bar is unity. ... 59

(9)

Figure 5.15 : Optimal distribution vector representation varying with SU traffic arrival rate.

Distribution fitting is shown in red. ... 60 Figure 5.16 : Channel utilization (channel busy period) and Busy period distribution is represented with SU traffic arrival rate. ... 61 Figure 5.17 : The target channel selection by secondary user by computing six permutations with basic aim of shortest handoff delay. ... 63 Figure 5.18 : State transition graph of three channel interruption caused by PU during SU spectrum handoff. ... 66 Figure 5.19 : Overview of a channel selection of the two-channel system. The occupation duration of primary and secondary user is denoted by “H/LPC”. ... 67 Figure 5.20 : The PRP M/G/1 queuing network model for two channel system... 69 Figure 5.21 : Transmission Latency for three spectrum handoff strategy with respect to PU Traffic arrival rate. ... 71 Figure 5.22 : Reactive Based spectrum handoff vs Proactive-based spectrum handoff. ... 72 Figure 5.23 : Total service time vs Primary user traffic arrival rate. Optimal in terms of SU service time for comparison purpose. ... 73 Figure 5.24: Graphical representation of PU busy period and system utilization with PU traffic arrival rate. ... 74 Figure 5.25 : Comparison of two target channel for spectrum handoff. Greedy algorithm is the best to select .Assume channel sensing time is 0, Ts = 0. ... 75 Figure 5.26 : Comparison of two target channel for spectrum handoff. Greedy algorithm is the best to select .Assume channel sensing time is 0, Ts = 0. ... 76

(10)

Chapter 1 : Background

1.1 Introduction

Cognitive radio network (CRN) is a novel idea for efficient spectrum utilization of radio spectrum resources. The main reason behind this idea was the survey conducted by the U.S Federal Communication Commission (USA regulator body for spectrum management) in [1]

shows the spectrum under utilization from 3 KHz to 300 GHz. The studies conducted by the spectrum task force and other spectrum regulatory bodies around the globe reveal low utilization of the spectrum in some frequency bands [8] resulting in spectrum holes. The spectrum occupancy measurements conducted by Shared Spectrum Company on the band between 30 MHz and 3 GHz at six locations in the U.S.A (2004) reveal the average occupancy is only 5 in urban area. Further, Fixed allocation assignment creates spectrum white holes, eventually leads to spectrum underutilization with low duty cycle in real time wireless communication system. Spectrum underutilization is the main challenge for spectrum management and their control &regulation authorities. New technology is needed to remove and overcome these problems and provides new methods to enhance spectrum utilization to greater extent.

This problem is explained in [3] in detailed as “Survey on spectrum utilization in Europe:

Measurements, Analyses and observation”. This experiment was conducted in three different locations such as in the cities of Czech Republic, and Paris in France in 2009.

Similar study conducted at Wireless research center Berkley (BWRC) showed that the spectrum is underutilized from 3 GHz to 6 GHz as shown in the Figure 1.1 [29].

Cognitive radio provides a solution to cope with all these problems. To effectively utilize the spectrum holes, the unused spectrum are shared among primary users PUs (licensed user) and secondary users (unlicensed users) SUs dynamically. The cognitive radio is developed on platform of Software Define Technology (SDR) and aware of the surrounding radio environment so to respond changes occurring in the network.

Figure 1.1Measurement form 0-6 GHz spectrum utilization at BWRC [29].

(11)

The main purpose of Cognitive radio technology is to increase Unlicensed Secondary User (SU) throughput in terms of various traffic parameters. This issue is investigated in this thesis.

The SU perform the spectrum sensing or information gathering process in which it selects the best vacant channel for transmissions of its data. The selection of a channel is based on various criteria consist of the shortest waiting time [27], the largest idle period [28] and the maximum throughput.

For smooth operation (SU) and to avoid interference with primary user (PU), the SU has to stop its transmission when the PU enter the network and request for vacant channel from the Base station.

In this thesis study is conducted on various channels assignment strategies for SU to enhance the spectrum flexible usage. SU Grade of Service (GoS) is the main performance measure which is thoroughly researched and analyzed in terms of call blocking, call dropping and call handoffs. Priority based M/G/1 and Markov methodology is used to explore these issues and challenges.

1.2 Thesis Outline

Chapter 2: Systematic literature overview is conducted regarding cognitive radio network and its basic operation is discussed.

Chapter 3: Provide overview of Queuing system and its application in cognitive radio network.

Chapter 4: Discussing spectrum management frame work for cognitive radio network and covering issues regarding spectrum sensing, spectrum decision, and spectrum sharing and spectrum handoff.

Chapter 5: Provide system models consisting of Priority Queuing using M/G/1 to characterize the performance of Secondary User in the network and simulation result can be used to grasp the operation parameter characteristics for secondary user.

Chapter 6: Conclusion and suggestions which can further extend this work for future work.

(12)

Chapter 2 :Overview of Cognitive Radio Technology

2.1 Spectrum scarcity issue

Radio spectrum is a valuable resources shared by different wireless service providers. It can be repeatedly use by frequency reuse techniques in global system of mobile communication etc.

Radio Spectrum management and utilization are the key research paradigms on which the most researcher are working on. The figure 2.1 has been used over 100 years for spectrum management.

The technical and policy aspects of spectrum management are extensively studied. The technical aspect of spectrum management is concern with technology and physical world phenomena that affects the spectrum utilization and policy aspect takes in to account of the economical and political factors. The International Telecommunication Union radio communication Sector has to manage radio frequency allocation for different emerging technologies and devise new standards for it. The spectrum is divided into several segments for specific wireless services. The licensed user has full right to divide the allocated spectrum into fixed number of frequency channels within a specific geographical area.

FCC is an independent organization of United State of America established by communications Act of 1934. Its main task is to regulate interstate and international communications by radio, television, wired, satellite and cable [2]. In 2002 FCC published

Figure 2.1: Schematic representation of division of the radio spectrum and radio ranges.

(13)

spectrum policy report that aims to adopt new technologies and create new technological resources. Radio spectrum is a limited resource by nature. The report concluded that Cognitive radio can enable better spectrum usage and improved network efficiency [28]. Thus cognitive radio can increase secondary market and effective spectrum utilization. FCC then published a report called Notice of proposed rulemaking (NPRM) using cognitive radio Technology [4].

Similar model is proposed by Ofcom in United Kingdom which defined the “command and control strategy” for spectrum management.

Figure 2.2 shows, that most of the licensed spectrum is unused and free. This reflects that license owner is not using the spectrum all the time and there are white holes/ white spaces/spectrum opportunity. The given spectrum is severely underutilized in the middle of radio spectrum. To solve the spectrum scarcity, to increase spectrum utilization and to fulfill the white spaces cognitive radio technology is used to achieve this goal. Similar study is conducted at Berkley wireless research center (BWRC) of Berkley University [29].

The measurement consist of a power spectral density of a radio signal between 0 and 6 GHz and samples were collected at 20 GHz for the duration of 50 seconds as shown in Figure 2.6. The radio spectrum is highly underutilized form 3 GHz to 6GHz resulting in wastage of the radio spectrum. Such problem creates spectrum scarcity for modern wireless communication system

2.2 The Cognition Cycle

The cognitive radio cycle first proposed by J.Mitola[51] and described by different authors in various paradigm covering multi features of cognitive radio. The CR is combination of

Figure 2.2 : Radio spectrum measurements [3].

(14)

intelligent signal processing and RF flexibility and as a result the cognitive radio cycle was implanted in radio equipments.

According to Simon Haykin [1] cognitive radio can be defined as:

“ Cognitive radio is an intelligent wireless communication system that is aware of its surrounding environment (i.e., outside world), and uses the methodology of understanding-by- building to learn from the environment and adapt its internal states to statistical variations in the incoming RF stimuli by making corresponding changes in certain operating parameters (e.g,transmit-power, carrier-frequency, and modulation strategy) in real-time, with two primary objectives in mind:

• Highly reliable communications whenever and wherever needed;

• Efficient utilization of the radio spectrum.”

The different actions taken by CR in cognition cycle are given in figure 2.3 [1]. To define it, a state machine embedded in cognitive radio to learn, adjusts and reacts to surrounding radio environment changes.

The radio will collect information about present operating environment as illustrated by outside world through direct observation. The importance of collected information is determined at Orient. According to that the cognitive radio will use either Plan or Decide alternative for operation. Wireless communication channels are considered to be unstable therefore cognitive user will adjust its resources through Act alternative. This process is repeated to improve its operations Learn and in creating new model states, seeking new alternatives.

Figure 2.3: Cognitive Radio Cycle

(15)

2.3 Network Architecture for Cognitive Radio Network

According to [16] the architecture of a cognitive radio and its functional framework can be presented. The basic cognitive radio network model is shown in Figure 2.4. The cognitive radio can be classified into two types; the primary network (licensed network) is the commercial network in the operation. There are two kinds of users, licensed (Primary User) and Unlicensed (Secondary user) in cognitive radio network. Licensed user is the owner of a radio spectrum. A customer pay monthly/yearly basis to government authorities. Licensed user is also called Primary user has the right and priority to use the spectrum anytime, anywhere in the world. The primary base station controls various operating parameters for PU. The operation of PU has not to be affected by unlicensed users. Unlicensed user or Secondary user has no authority to use any radio spectrum without permission. Cognitive radio provides an opportunity for Secondary user to use the spectrum, whenever Primary user is not using the radio spectrum.

.

Cognitive radio using advance wideband access technology can access both the licensed and unlicensed bands of communication channels. The Primary network uses licensed spectrum for operation. The SU has to monitor the presence of PU in the network. In absence of PU the spectrum band can be used by SU. Hence a complicated frame work of spectrum management is developed for SU to utilize the vacant band of the PU.

Cognitive radio designed to provide better QoS for secondary user applications. Spectrum management policies are devised to meet the QoS requirements of Secondary Users. Spectrum management techniques are given below

 Spectrum Sensing (Monitoring the presence of PU)

Figure 2.4: Cognitive Radio Network Model

(16)

 Spectrum decision (Allocation of vacant channel)

 Spectrum Sharing (Avoiding Multiple User Colliding)

 Spectrum Handoff/Mobility (To move SU due to presence of PU to other vacant channel)

2.4 Standard and Regulation

Cognitive radio standards are under careful investigation. The IEEE standards coordinating committee (SCCI 41) on dynamic spectrum accessing work is related to cognitive radio network [5]. The responsibilities of IEEE standard coordinating committee 41 (Dynamic Spectrum Access Networks) consist of maintaining the standards developed by the committee in accordance with IEEE-SA standard board operating Manual. Further, to cooperate with other standard developing organizations. For next generation radio network and spectrum management IEEE initiated 1900 Standards committee. The IEEE P.1900 committee was formed in 2005 to formulate standards for new emerging radio networks.

2.4.1. IEEE standardization on cognitive radio

IEEE standardization related to cognitive radio include IEEE 802.22 Working group for the development of physical (PHY) and Medium Access Control (MAC) for Wireless Regional Area Network (WRAN) operating in TV broadcast band .WRAN's operate in unused channels in the VHF/UHF TV bands between 54 and 862 MHz[7].

The motivation of developing IEEE 802.22 standard was to utilize the unused Digital T.V spectrum to provide broadband services in rural areas. The accessibility to broadband services is not that critical in suburban areas as compared to rural area where establishment of DSL network is expensive for few customers. The operating TV bands chosen to provide broadband service have high-quality propagation characteristics covering larger area. In U.S.A many T.V channels highly unoccupied [8]. The 802.22 network will provide high quality voice and data services with suitable QoS. The cognitive radio networks for the first time deployed in U.S.A for commercial operation. The IEEE 802.22 standard is devising rules and regulation which can accommodate system specification consisting of frequency range of operation from 54 -862 MHz and suggestions are circulated to extend to 900 MHz [11].

2.4.2. ITU-R standardization on cognitive radios

ITU-R is conducting standardization activities related to cognitive radio networks. The standardization activities are carried out in the Study Group 1 (SG 1) which is responsible for spectrum management [6]. The IEEE is important sector member and contributes to ITU-R standardization activities. The ITU Radio communication Assembly in 2010 sent Question ITU-

(17)

R 241-1/5 on cognitive radio system in the mobile service. ITU-R 241/8 lists the following issues to be studied in ITU-R WP 5A [6].

2.5 Industrial Applications

There is rapid transition from analog to digital technology in TV broadcasting. Many countries around globe currently broadcast both analog TV and digital TV (DTV) simultaneously. Most of analog services will be switched-off at different times in countries. There will be vacant channel or white spaces in the TV bands. The FCC has proposed in U.S to allow unlicensed working in the white spaces in the TV spectrum: 76-88 MHz, 174-216 MHz, and 470-608 MHz’s. The main issue in the implementation of cognitive radios on TV bands is to reduce interference between licensed and unlicensed user [8].

2.5.1 Adapt4's XG1

Adapt4 developed first commercial CR product available in the market with XG1 label in 2004 [8]. XG1 is an OFDM-based system operating in licensed frequency band in 217-220 MHz as secondary user. The throughput rate of Adapt's XG1 is 192 Kbps. The CR network deployed with XG1 supports point-to-point and point-to-multipoint with multi-hopping architectures. The network identifies vacant channels and uses them for transmission and avoid channel when other activity is detected.

Adapt4's XG1 consist of following capabilities [Adapt4]:

 Frequency hopping,

 Dynamic Power Management,

 Automatic Configuration

 Dynamic Frequency Selection

The XG1 cognitive radio network constantly observes the activity of the license user in the band and identifies unused channel. In CR OFDM systems a set of sub-carriers of 45 is created, each of width 6.25 KHz and rapidly hops among them using each channel for 10ms. When another licensed user is sensed, the network stops using the carries until they become vacant again. The XG1 equipments detect the licensed users and forward this information to a central station.

Figure 2.5 : XG1™ Cognitive Radio

(18)

2.5.2 Cognichip

France Telecom developed simple low cost radio device called Cognichip. It has to be operated in the ultra high frequency (UHF) spectrum of 470-870 MHz described in [9]. The Cognichip can detect spectrum holes in the TV spectrum and can support centralized architecture. The network consists of base station and User devices. The user device with transceiver chip measures of radio signal strength indicator (RSSI) for spectrum occupancy. The sensing time for one channel is 24 ms. The user device cannot receive data during sensing the spectrum.

2.5.3. Rockwell chip

The Rockwell Collins has developed a broadband spectrum sensor for frequency band 30 MHz -2.5 GHz [10]. It is power efficient with power consumption is below 2.5 W and scan the spectrum at 18 GHz.

2.6 xMax Commercial Cognitive radio Network

The xG Technology developed first industrial commercial xMax cognitive radio cellular network and presently it is deploying in Florida for U.S Army training center. xMax will provide mobile communication service with larger coverage area. The network deployed will consist of base station of BSN-250, a central switching center xMSC and special handset of TX70 handsets. A reference model of xMax cognitive radio network supporting data and voice service is given in figure 2.6 [21].

Figure 2.6 : Network Architecture of xMax cognitive radio network.

(19)

Features of xMax Network:

 Base station (BSN) is 18 channel mobile VOIP transceiver device .It channelize the spectrum from 902-928 MHz into 18 discrete channels, and is only in used when there is traffic for a mobile equipment registered with a particular channel.

 Access Network Gateway (ANG), called the xMSC is the central switching center that provides service of call processing, IP packet delivery services, mobility and other signaling related functions.

 The xMax system provide soft handoffs features with capability of (make-before-break) time slots are acquired before breaking a connection resulting in reliable roaming and a seamless user experience.

 Ethernet switch aggregate BSN links in use.

 Firewall provides NAT (network address translation) services.

 SIP proxy server provides SIP call control, xG’s SIP message compression technology.

 Network Monitor handle end to end network management and monitoring services.

 Proxy DHCP server is used for IP addresses services.

(20)

Chapter 3 : Queuing Models

3.1 Overview of the Queuing Model

Queuing or waiting lines can be found everywhere. Queuing for buying food at supermarket, to withdraw money from bank or a telephone waiting to be placed through to receiver are day to day examples. Queuing happen because the demand for a particular service is higher than the server can cope with it. The Queuing theory developed from roots of studies conducted by A.K Erlang and A.Markov on the stochastic processes. It is used to model broad variety of systems including computer networks, telephone systems, and military operations. In early 1900 A.K Erlang, conducted experiment with variable demand of telephone traffic. Later he published a report in 1917 elaborating delays in automatic dialing equipment .

The Queuing theory [28] is the study of queues or waiting lines. It has been extensively used in different system covering fields such as:

 Supermarket has to decide the number of cash outlets to be operative.

 Gasoline station has to decide the number of pumps and attendants on duty.

 Bank must have to decide the number of teller windows for customer service.

3.2 Basic Queuing Model

The basic queuing system can be illustrated as customers arriving for service, waiting for service if the server is busy and leaving the system after service being completed as shown figure 3.1.

Figure 3.1 Basic Queuing Model.

(21)

The basic queuing model depicted in figure 3.1 can be identified some basic elements of the system as [29]:

 Input process: describe the input process in terms random variables representing either the number of arriving during a time interval or the time interval between successive arrivals. Furthermore, the distribution can also be used to determine the arrival of customer to the system. If the arrivals of customers and the offer of service are according to plan, a queue can be avoided. If customer arrives to the system and leave it without service is referred as Balked.

 Service Mechanism: it involves the number of servers, the number of customers being served at any time, and the duration of service and its modes. In network of queues more than one servers arranged in series or parallel combinations. Random variables are used to characterize the service times, and the number of servers. The processing time is represented by appropriate distribution function.

 Queuing: the number of customer waiting for service is important point of consideration.

The waiting room or queue length can be considered infinite. The realization of such queue is hard in real network such as telecommunication networks.

 Queue discipline: representing the way in which queue is organized. The rules which are devised consist of inserting or removing customers from the queue.

3.2.1FIFO Queuing:

Main characteristics of FIFO given in figure 3.2 queuing discipline:

 All arriving packets are placed in a queues

 Transmission of packets in order of arrival

 Packets are discarded when buffer is full

 Delay and loss of packets depends on inter-arrival times and packet lengths

 Flow can interfere with each other

 Random drops due to malicious monopolization : as one flow sends packets at a high rate and fills the buffer

 Fairness among customers is not achieved with packets from higher priority class are buffered as long as there is space discarding the packets from lower priority packets.

Figure 3.2 : FIFO Queuing Model.

(22)

3.2.2 Priority Queuing

: following are the characteristics of the priority queuing is presented in figure 3.3:

 Packets are served according to priorities set by system.

 Packets are separated and placed in different buffers according to the priority classes.

 High priority is set for packets for low delay requirement applications

 The preemptive selection of packets from highest priority queue can be disruptive for the operation of queue of low priority class.

 Randomly selection of packets from low priority class of queue can be helpful for smooth operation of both queues.

3.2.3 Fair Queuing

: The fairness drawback of FIFO queuing is effectively solved by fair queuing algorithm.

 To provide equitable access to the transmission bandwidth with each packet flow has its own flow.

 Bandwidth is equally divided between flows; each flow is transmitted in turn round robin fashion.

 At some instant time some queues will be periodically be empty with irregular traffic giving other flow better share of bandwidth.

 In case of a fair packet switching network given in figure 3.4 packet lengths into account.

 For fair share bandwidth the transmission capacity is divided between different numbers of flows. These different priority flows are serviced by server in a way that reduces overall waiting time, overall service time to achieve fairness among users.

The introduction of fairness among users results in improvement of system utilization.

Figure 3.3 : Priority Queuing Model.

(23)

3.3 Operation characteristic for Basic Queuing Model

David G. Kendall proposed notation for queuing models later named after him called Kendall’s notation with expression A/B/n K/T.

Where:

A = inter-arrival time distribution B= service time distribution n= number of servers

K = System total capacity or number of waiting customers T= queuing discipline

For A and B the following abbreviations have been developed representing various probability distribution describing arrival and departures to a system under consideration.

M (Markov): denote exponential distribution with markov property (memoryless) D (Deterministic): Constant time intervals

Ek :Erlang-k distributed time intervals

Hn :Hyper-exponential of order n distributed time intervals C_OX : Cox-distributed time interval

Ph: phase – type distrusted time intervals

GI : General. Arbitrary distribution of time intervals

= arrival rate of customers in system

= service rate of customers in system N= number of customers in system

Nq= average number of customers in queue of the system T = Mean time spent in the system by customers

W = mean waiting time

Figure 3.4 : Fair Queuing Model.

(24)

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 0

0.5 1 1.5 2 2.5 3

Time

Distribution of arrival

Exponential distribution of interarrival traffic

arrival rate = 3 arrival rate = 2.5

 ^ = offered traffic

The M/M/1 is the simple queuing system (with FIFO service) can be described as follow: a single server with infinite queue length with customer inter-arrival rate is exponentially distributed with parameter  and the customer service times are exponentially distributed with parameter is shown in figure 3.5.

Figure 3.5 : M/M/1 Queuing Model.

The M/M/1 model depicted in figure 3.5, assumes that the number of arrival of customers or packets (data network) for a given interval of time t follows a Poisson distribution with parameter of product of t. let for n arrivals within a time interval t, the probability distribution function P (n) is given as:

(3.1)

The probability density function of the inter-arrival times is given by e^^^t for t >=0.

This is called the negative exponential distribution function with parameter shown in figure 3.6.

( t) t

( )

ne

P n n



^



Figure 3.6 : Exponential traffic arrival distribution.

(25)

The property of stationarity and lack memory can be applied to M/M/1 systems such that a Poisson input arrives to the server is independent of each other or the state of system. The probability of arrival of a unit to the system depends only on the length of time s without considering the initial state or the specific history of arrival preceding it. The queuing system with Poisson input as Markovian process (denoted by M) is considered.

Examples of Queuing Systems

M/M/m: m stands for servers rather one server (a bank queue with multiple teller)

M/G/1: Similar to M/M/1 queuing model stands for servers rather one server (a bank queue with multiple teller with general service time distribution).

M/G/1 Queuing Model

The M/G/1 queuing system is a single server queuing system in which the arrival of customer follows Poisson distribution with rate of and service time S for each customer follows general distribution with mean E[S] [31].

Let to observing the queuing system for very long time T for a system . During this long period of time there have T customers arriving to the queuing system, each customer on average take E[S] minutes to be served and  (fraction of time server is busy).

(The time server is busy) (3.5)

3.4 Priority queuing using M/G/1 Model

The practical extension to the classical M/G/1 queue is the preemptive resume priority M/G/1 queue. The queues consist of two or more classes of services. Each customer belongs to a single class of service. A predetermine priority ranking among the classes of service is set shown in figure 3.7. Each class of service has its own distinct arrival rate (either exponentially distributed arrival time) and service time distribution.

Class 1 Lowest Priority Class

1 2 3 p

Figure 3.7 : M/G/1 Priority Queuing Model [ ]

T E S

T

   ^

(26)

Mathematical Notation for M/G/1

 Xi =random variable for service time of priority i customers

 Ri= random variable for residual service time of priority of i customers

 _i=arrival rate for priority i customers (Poisson arrivals)

 _i= _iX_i=utilization of the channel by customers of priority of i

 _i=

1 i

j_ i



= utilization of the channel by customers of priority 1 to j

 W = random variable for the waiting time of a customer of priority i waiting until service _i begins

 N = random variable for the number of customers in queue i (not for customers in the _i channel)

T = random variable for the time spend by i customer in the systems from arrival until the i

completion.

The average waiting time for customers of different class of services and average number of customers for each class of service in the queue is calculated. This will depend whether priority discipline in queue is Preemptive or Non-Preemptive.

Non-Preemptive discipline: A non-preemptive denoted by head-of-line-NP queuing discipline require customer that begin service to complete its service without pause, even if customers of higher priority arrive in the mean time. The average queuing time of a customer depends on the customer’s arrival rate of lower priority classes.

Mathematical Notation for Head of Line non preemptive (HOL-NP) Expected residual service time of a job in service R = number of priority classes

Mean time in the system for class 1 job

Mean time in the system for class j job (j=2...R)

2 0

1

1 ( )

2

R

i i

i

W E S





0

1 1

1

( ) 1 W E S W

  



0

1 1 1 1

1 1

( )

(1 ) (1 )

j j

i i

W E S W

 

 

 

 









(27)

Preemptive Resume Priority discipline: A preemptive resume priority is denoted by head of line preemptive (HOL-PR) involve a customer arriving at the queue find a customer of lower priority in service, the arriving customer preempt the customer being served and begin service immediately. A preempted customer will resume service at the point at which its service was suspended as soon as there no higher priority customers. This all process is also called the preemptive resume.

Mathematical Notation for Head of Line Non Preemptive (HOL-PR)

Expected residual service time of a class j job (j=1….r) Mean time in the system for class 1 job

Mean time in the system for class j job (j=2….R)

Preemptive None Resume Priority discipline: In this scenario the customer with higher priority being served after arrival to the system (presently serving low priority customer) preempting the job of lower priority class. After finishing the higher priority class job system will start afresh without remembering the service that has already been provided.

3.5 Selection of the Queuing Model for Thesis

Working with priority based (Queuing) for cooperative communication (CRN) in cognitive radio is a challenging task. Several research questions related to the cognitive radio environment as follow.

1) Which Priority model should be used and how to work with?

2) Which model will follow the real time feasibility and implementation?

3) How to prioritize different users with different priority classes?

4) Which type of (Queuing Distribution system) will be used for input and outgoing link in real time?

5) How to deal with Queuing management issue with respect to incoming users? Etc…

2 0

1 ( )

j 2 j j

W   E S

01

1 1

1

( ) 1 W E S W

  



0 1

1 1

( ) 1

1 1

j i i

j j j

i i

W W E S

 

 

 

 

 

   

   

 

 



 

(28)

All such research questions need explanation with an acceptable accuracy. To find out the most feasible Queuing system model to answer theses research questions. An Engineering and mathematical literature contains 3 models, which 8 possible combination are made. The simulation for all possible queuing system classified in terms of call loss ratio for the following data as listed below. The simulation tool (TOOLS_V1.3) is developed by Markus Fiedler at the department of Telecommunication systems at BTH, extensively used for queuing system analysis.

1) Mean Inter-arrival time of users = 5 min 2) Mean service time = 4 min

3) Number of batches = n = 100

4) Number of arrival Per batch = k = 1000 5) Radom numer generator seed value = 0 6) Simulation warm-up period = True = 1 7) Queuing length is variable, dynamic

The simulation run for 8 possible distribution, and collected observations which are being listed in Table No.3.1. The call loss ratio and observed the statistical confidence level signify results

\

SIMULATION OBSERVATIONS:

Distribution Type Call loss ratio Confidence interval

D/D/1/K7 0 0

D/G/1/K7 0 0

G/D/1/K7 0.000155 ± 3.62559e-005

G/G/1/K7 0.00173 ± 0.00013151

D/M/1/K7 0.005312 ± 0.000276096

M/D/1/K7 0.008698 ± 0.000355648

G/M/1/K7 0.015034 ± 0.000460548

M/G/1/K7 0.017377 ± 0.000570848

M/M/1/K7 0.038568 ± 0.000859249

Table No.3.1: Different Queuing Systems Distribution Types are tabulated with respect to call loss ratio and confidence interval. Call loss ratio is in increasing order which is inherently related t QOS and GOS of system users. * M/G/D - Poisson/General /Deterministic Distribution respectively.

(29)

ANALYSIS: For D/D/1 and D/G/1 queuing system the input distribution is deterministic and service time distribution is deterministic. These models are called as Deterministic System showing constant and non-varying behavior with call loss ratio equal to zero. Confidence interval in this case is negligible.

For M/M/1 as in table, inter-arrival rate and service rate are Poisson (Exponential) processes, showing pure random and stochastic nature. The errors and call loss ratio is maximum for M/M/1 queuing system. Call loss ratio for different queuing distribution system is plotted in Figure 3.8.

Best Queuing Systems: D/D/1 and D/G/1 Queuing model with deterministic input distribution is unrealistic consideration in real time Cooperative communication (CRN). So they are Real time unrealistic.

Worst Queuing System: M/M/1 Queuing model are considered less accurate and more problematic in terms of GOS (No Real time feasibility).

Feasible Queuing System Requirement: In Priority based Cooperative communication (PBQCRN) each user (PU and SU) is treated by server with different priorities. Different Priority classes have different service time distribution, a new theory is needed to characterize General Service time Distribution (G-Distribution) for different users.

REQUIREMENTS:

a) Input arrival rate of users follows Poisson (Exponential) distribution b) Priority based output service time will follow General distribution.

Figure 3.8 : Graphical representation of Call loss ratio experienced by users with different Queuing Distribution systems.

(30)

This will fulfill the need and requirements for real time implementation of cognitive radio networks. M/G/1/K is one such model which fulfill over requirements and shows real time implementation accuracy and feasible results. The M/G/1 model is selected to carry out our research in this master thesis. Figure 3.9 depicts the motivation strategy for the thesis.

(Markov)

Figure 3.9: Simulation Strategies for Model selection.

(31)

Chapter 4 : Spectrum Management Frame Work for cognitive radio network

4.1 Radio Spectrum as a Resource

The radio spectrum is valuable resource of nature and essential part of society’s infrastructure. The radio waves have wide range of applications covering sectors such as social, cultural, scientific and developmental proposes. It consists of emergency services, defense forces and air traffic control, broadcasting scientific research; so on. The demand for the radio spectrum allocation for the viewers and listener to radio and television and new developing technologies is high. The technology for implementing radio wave communication can easily be developed and deployed for commercial use. The radio wave spectrum is defined in technical term as portion of electromagnetic spectrum that carries radio waves. The ranges of frequencies which identify the boundary of radio spectrum are from 9 kHz to 3000 GHz as illustrated in table 4.1.

The other important characteristics of the spectrum consist of the amount of information it carries and it propagation characteristics. The propagation characteristics describe the behavior of radio waves in the spectrum. In different radio frequency bands depicted in table 4.1, waves

Table 4.1: Radio spectrum bands and its propagation characteristics

(32)

act differently in term of its ability to hop, spread and penetrate.The lower frequency has less bandwidth capacity to carry information then higher frequency. It can be used in for radio navigation in the range of 3 kHz to 30kHz.The most of modern communication technologies such as GSM, CDMA and LTE utilize higher frequency bands due to availability of more bandwidth then lower frequency bands.

4.2 Radio Spectrum Management Design Issues

The developments in information and communication technologies have opened up new range of useable radio spectrum, enabling greater access to new allocations and assignments. The spectrum being an economic resource is both non-storable and non exhaustible and unlike oil it will never run out. It may become congested. The spectrum regulator authority has to effectively meet the demand of radio resources requested by public and commercial wireless communication companies an effective strategy in the form of spectrum management which can be helpful utilizing this scare resource among different masses of society [26].

The dynamic spectrum access is defined according to standard of I.E.E.E 1900.1 “as method by which radio system dynamically adapts to select operating spectrum to use available in the form spectrum holes with limited spectrum use right”. It requires advance technology for opportunistically accessing the unutilized bandwidth for a service. A DSA radio has property of agility and flexibility such that it can operate in different spectrum bands at time and support various many different transmission standards.

The DSA technology also include cognitive radio technology which may be define according to IEEE 1900.1 standard as “A type of radio in which communication systems are aware of their environment and internal state and can make decisions about their radio operating behavior based on that information” The secondary user based on software radio define technology or cognitive radio technology consist of unique features for detection of the spectrum hole or white spaces (unutilized portion of bandwidth). It makes use of this spectrum or hole for their connectivity and transmission of data. The secondary also monitor the communication environment and independently adapt the parameters accordingly to communication scheme to maximize the quality of service for secondary user.

The two main characteristics of secondary user which summarize its operation in dynamic spectrum access network are as follows [28]

 Cognitive Capability: Refers to the ability of radio technology to collect information or sense in radio environment. The monitoring of the radio environment consist of not only observing the power in frequency band of interest , but additional techniques of learning

(33)

and action decision are required to gather information consisting of modulation schemes or transmitter power.

 Reconfigurability: With advance hardware and software ability, the secondary user can be reconfiguring to adapt to changes occurring in dynamic radio environment.

The cognitive capability and reconfigurability enables a secondary user to utilize the unused spectrum in the network. In figure 4.1 a typical scenario is depicted, the secondary user network architecture consists of Primary network and the secondary user network [3].The primary user activity is controlled by primary base station. With centralized secondary user network a CR base station control the operation of a cognitive user. The spectrum broker manages spectrum resource among different CR networks.

The cognitive radio operating in heterogonous network environment faces many technical challenges. The challenges can be classified into four main topics as follow:

 Spectrum Sensing

 Spectrum Decision

 Spectrum Sharing

 Spectrum Mobility

Figure 4.1 Cognitive radio Network Architecture.

(34)

4.2.1 Spectrum Sensing

Spectrum sensing is the key feature of cognitive radio. The spectrum sensing gives SU to observe the available radio environment for spectrum holes. It is closely related to other spectrum management functions also to layering protocols to provide information on spectrum availability. Nowadays research focused on the interference avoidance problem. The cognitive radio cannot perform spectrum sensing and transmission operation due to hardware complexity and propagation environment [16].

The spectrum sensing consists of PU detection by SU. When the spectrum sensing is performing on wide frequency range for their transmission is referred as out of band sensing).

When the detection process is executed during transmission of data, the cognitive radio user avoid interference is referred as (in-band sensing). In most of application devices energy and feature detection methods are commonly used for PU presence in the network [12]. In energy detector, the energy in the spectrum over an observation time window is collected and decision is made regarding presence and absence of PU with respect to pre define threshold [13]. It is simple and efficient spectrum sensing technique operates without the prior information of Primary user (PU). Furthermore its performance worsens in fading channels as it cannot differentiate between noise and primary signal. The cognitive radio network devised functionalities for spectrum sensing are as follow [32]:

 Primary User Detection: The secondary users observe the surrounding environment for detecting the presence of primary user transmission and accordingly identify spectrum availability.

 Cooperative spectrum sensing: Spectrum sensing is performed by as SU and a group of SU in cooperative fashion. Each secondary user observes individually the presence of PU and sends its result to central station. The central station fuse individual spectrum sensing result and make final decision regarding the presence of PU. To cope with multipath fading and shadowing effects different cooperation methods are proposed [15].

The other technique is feature detection technique is that of its robustness to the uncertainty in noise power [14].

4.2.2 Spectrum Decision

The spectrum decision functionalities are similar to that of spectrum sharing such that in the spectrum decision the resource allocation based on application service requirement. The

(35)

researches had extensively studied the spectrum sharing and explored QoS issues for secondary user [31]. The unique feature which distinguishes the spectrum decision from spectrum sharing is that the spectrum sharing is performed for short period of time in middle of a communication call within the spectrum band [30]. It is carried out for packet based or a time-slot scheduling on other hand the spectrum decision is event or connection based operation. Hence comparing the operation of spectrum sharing, the spectrum decision considers traffic statistics and channel characteristics for long period of time.

The main functionalities of spectrum decision consist of:

Spectrum characterization: secondary user network or (CRN) characterizes the spectrum band by taking into consideration the receive signal strength interference and number of users currently residing in the Spectrum Selections: according to the observed spectrum availability CRN allocate the best spectrum band to satisfy QoS requirements.

The spectrum sharing operates in intra-spectrum boundary with its operation confined to a specific spectrum. The spectrum decision is inter-spectrum operation such that available spectrum is distributed over a wide range and due to some technical requirement (fading or QoS requirement) the shifting from one band to other band induces delay or transmission latency leading to degradation in service quality.

The selection of channel for transmission of data consider various factors such as lightest traffic load , the shortest expected waiting time , the largest idle probability .The two selection schemes which are extensively studied for spectrum selection take account of the traffic statistics of primary and secondary user are as follow:

Instantaneous sensing-based spectrum selection method: the operating channel selection is done by the secondary user according to instant or short-term sensing. Probability-based spectrum selection method: The operating channel is selected based probability measures of the long term observation outcomes. This scheme result in shorter overall transmission time for the secondary user as it had not to scan the huge spectrum. The secondary user had to avoid in selecting the busy channel with high probability.

4.2.3 Spectrum Sharing

The necessary coordination is needed between secondary for utilizing the communication channels with minimum interference. The spectrum sharing in this respect play key role. The spectrum sharing with most of MAC protocol functionalities maintain the QoS requirement for secondary user and coordinate activates of multiple access of the communication channel and its allocation.

The secondary user operates in challenging environment as it coexists with primary user and different fading environments. Thus we can summarize, the spectrum sharing focus on two basic following functionalities:

 Spectrum Access: It enables secondary users to share the spectrum resources by accessing it effectively according to certain rules.

(36)

 Resource allocation: The secondary user selects the spectrum band which fulfills the QoS requirement for certain application. The other factor which it take in account while allocating the resource is transmission power to avoid interference.

The architecture for spectrum sharing in the Secondary user network is based on access technology defined according to [33] evolved during course of time for resource allocation in spectrum sharing can be classified as either as

 Centralized spectrum sharing: a central station control the spectrum allocation and access procedure. Moreover the central station can grant the spectrum access to users for specific amount of time for small area. This technique is coordinated effort carried out by secondary users to reduce the collision or interference while accessing free vacant channels.

 Distributed spectrum sharing: each SU perform the spectrum allocation and access based on (local and global) policies. A different network uses distributed solutions such that different Spectrum Managers (base stations) compete for allocation of spectrum according to the QoS requirement of Secondary users.

4.2.4 Spectrum Mobility

The secondary user is outsider or visitor in the network. When the primary user enters the network request for a vacant channel then the secondary user has to stop its transmission. The secondary user communication has to be continued in another vacant channel give a spectrum mobility phenomena. The main functionalities carried out in mobility management consist of spectrum handoff and connection management. In spectrum handoff, a secondary user shift from occupied channel to free channel due to arrival of primary user in the network. The secondary user using its reconfigurablity property adjusts its operating parameters (frequency, modulation types)[34]. Where as in connection management the secondary user interact with different protocol layers to maintain QoS for specific application. In addition to Primary User activity in the network, spectrum handoff may occur due to flowing reasons:

 The secondary user drop its connection due to mobility of user

 The current spectrum band cannot provide the QoS requirement

(37)

4.3 PRP M/G/1 Queuing Network Model

The secondary user faces many challenges in its operation in licensed network due to quality of Service (QoS) requirement for applications [35].

For smooth operation each SU had to address challenges as follow:

 Availability of the spectrum (to avoid interference with PU)

 Best Available channel (QOS requirement for different applications)

 Vacate with entry of licensed user in the network

 Seamless Communication.

The challenges for secondary user operation are transformed into relationship between spectrum management functionalities for cognitive radio is shown in figure 4.2. Consider a cognitive radio network where primary and secondary user operates on M1 independent channels. The secondary user uses spectrum sensing algorithm to select M2 from out M1 channels. The secondary user on requirement of QoS for a application select M3 channels out from M2 suitable for transmitting data. Then on the spectrum decision algorithm base, a secondary user can decide the operating channel from M3 channels.

Figure 4.2 : Relationship between spectrum sensing, spectrum decision, and spectrum sharing and spectrum mobility.

(38)

Based on the spectrum management functionalities the secondary user can sharply adjust its operating parameters to cope changes occurring in the environment. Thus secondary can increase its data throughput at same time minimize interference with primary user.

The PRP M/G/1 queuing model is used to characterize the spectrum usage behavior with multiple handoffs strategies between primary and secondary users. Based on this model, the overall transmission time with multiple handoffs can be evaluated. The key features for PRP M/G/1 queuing model are listed below:

 The primary users have the preemptive priority to interrupt the transmission of the secondary user.

 The interrupted secondary user (due to arrival of primary user) can resume the unfinished transmission of data.

 A secondary connection may be interrupted due to random access request of primary user for vacant channel.

(39)

Chapter 5 : System Model And Simulation

5.1 PRP M/G/1 System Model for calculating average waiting time of Secondary user

In the universal mobile telecommunication system, the size of packets varies to carrying the payloads for different types of service such as data and voice services. Since voice communication is sensitive to delay so the sizes of packets are small. Whereas for short messaging service the packet can be long in size due to its tolerance to delay.

It is assumed that the communication channel is divided into equal time slots. And each user in the network had two types of packets for voice and data communication. In the telecommunication network voice packets are prioritized most then data packets. These different types of packets are stored in different types of queue of infinite length. Each queue is processed in first-in-first-out (FIFO) order. The priorities are also set among PU and SU. With PU is preferred of using communication channel. In this study it is assume that the size of voice packets is shorter the size of data packets. To realize this condition for PU conditions are set such that L1<L0 and L3<L2 where L1 is the sizes of voice packet and L0 is the size of data packets for PU. While for SU L₃and L₂ denotes the size of voice and data packets respectively.

The analysis of arrival of PUs on the average waiting time for SUs is examined with help of two scenarios consists of:

 Multiple PU and Single SU

 Single PU and Single SU

A preemptive priority queuing model M/G/1 is employed to analyze the average waiting time of packets for the SU in the light of queuing theory, when it operates with single or different number of Primary users.

5.1.1 Multiple Primary User and Single Secondary User

The system considered in this scenario consists of multiple PUs and a single SU with a time slotted [40] cognitive wireless networks where PU is the Licensed user. The transmission channel is divided into time slots to be used by voice and data packets as shown in figure 5.1.

The PU being licensed user access time slot of resources is at highest priority. The access to the transmission channel can be described as Poisson process with arrival rate λ.

(40)

The PU starts its transmission always at the beginning of a time slot. As matter of fact the transmission is not always occupied by PU. The SU can effectively utilize it. With lower priority in the network the SU has to check the availability at the beginning of time slot. The SU can utilize the free time slot in the absence of PU in transmission channel. When the presence of PU is detected, the SU has to wait at least one time slot before it may start its transmission. Let the SU perform perfect sensing for short duration as shown in figure 5.1.

Figure 5.1

During this short interval of spectrum sensing the SU collect all necessary information regarding presence of PU in the particular time slot and then decides whether to transmit or not.

A cognitive radio network under study consist of three PUs denoted as P1, P2, and P3 occupying transmission channel in fixed time slotted manner as shown figure 5.1. The SU can only utilize the vacant time slot of transmission channel after the sensing period. It cannot start its transmission in middle of time slot after being free by PU sometime prior to the end of the slot.

To model this behavior traffic from PUs is defined as a sequence of special packets that uses one time slot each as shown figure 5.2. The SU can only transmit voice and data packets when the queue of special packets is empty. Hence the obtained three priority classes are with special packets from PUs given first priority over voice packets with second priority and data packets third priority of SU.

P1 P2 P3

Sensing Period Busy Slot

Free Slot

Figure 5.1 : Cognitive radio network using TDMA slot for communication.

Priority Queuing Based Spectrum Sensing Methodology in Cognitive Radio Network